用分解的方法构造二维数据处理的并行算法

Pub Date : 2020-09-01 DOI:10.17223/19988605/52/14
O. Klimova
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引用次数: 0

摘要

本文提出。该工具旨在表示二维卷积和相关、二维离散傅立叶变换(DFT)和其他结构类似的操作的计算组织的各种变体。给出了该工具的创建方法,其特点是使用群论分解方法。所获得的工具是一种参数化的并行计算组织的时空描述,为将算法参数和体系结构参数结合在一个形式化描述中提供了基础。该工具在体系结构上也是独立的,允许我们实现算法和体系结构的并发探索阶段。这一研究阶段是设计先进计算系统的重要组成部分,其实现有助于提高并行计算的效率。本文提出的形式化工具的开发是基于使用所提出的分解方法研究算法的内部时空结构。使用这种方法,最初是为构建数字信号处理基本操作的组合形式而开发的,由于其扩展到二维数据处理领域而成为可能。本文提出了这样一种扩展,使二维数据的处理可以转移到一个参数化坐标计算环境(CCE)中,用于描述并行计算。由于实现了扩展,形成了一个旨在研究二维数据处理计算操作的内部结构以检测其组成形式的动作系统。这些行动是在建议的方法范围内制定和实施的。对所得到的形式进行了结构分析,发现了它们的模型性质。因此,向组合运算形式(CFO)的过渡使得二维数据处理中计算组织的表示形式从算法形式转变为模型形式成为可能。给出了该类各种操作的计算组织CFO (A ijp (t 11, t 21), CCE i (j, p))的一般模型描述,其显著特征是计算沉浸在参数化协调计算环境中。所提出的描述的组成部分是对序列算法A i (t)进行分解、浸入空间环境和时间压缩后得到的算法A ijp (t 11, t 21),以及参数化的坐标计算环境CCE i (j, p)。处理二维数据描述的参数x (t, t 2)给定的H组订单N = N 1×N 2介绍如下:t = t 2 + 1 N 2, N 1 = 11 H L 1, N 2 = 21 H L 2, t 1 = 0,…,N 1 - 1, 11 = 0, t…,H 11 - 1 j 1 = 0,…,L 1 - 1 t 2 = 0,…,N 2 1, 21 t = 0,…,H 21 - 1 j 2 = 0,…,L 2 - 1。从表示二维操作的算法形式到表示二维操作的模型形式,我们不仅看到了操作的内部空间结构,而且有机会通过参数h 11, L 1和h 21, l2综合各种计算组织变体对其进行配置。这样的机会使人们能够解决旨在提高并行处理效率、降低其复杂性以及增加分析所处理数据的灵活性的紧急问题。
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Decomposition approach to the construction of parallel algorithms for processing of two-dimensional data
presented in this paper. The tool is intended to represent a variety of variants of computation organization for two-dimensional convolution and correlation, two-dimensional discrete Fourier transform (DFT), and other operations structurally similar to them. The creation way of such a tool, characterized by the use of the group-theoretical decomposition approach, is shown. The tool obtained is a parameterized, space-time description of parallel computation organization and provides a basis for combining algorithmic and architectural parameters in a single formal description. The tool is also architecturally independent and allows us to implement a stage of concurrent exploration of algorithms and architectures. This research stage is an integral part of the process of designing advanced computation systems and its implementation leads to improving the efficiency of parallel computing. The development of the formal tool presented in the article is based on the study of the internal space-time structure of algorithms using the proposed decomposition approach. Using this approach, which was originally developed for constructing compositional forms of the basic operations of digital signal processing, became possible due to its extension to the field of processing of two-dimensional data. The paper presents such an extension that made it possible to transfer the processing of two-dimensional data into a parameterized coordination-computing environment (CCE) intended to describe parallel computations. As a result of the extension realized, a system of an actions aimed at studying the internal structure of the computational operations of processing of two-dimensional data in order to detect their compositional forms is formed. These actions are developed and implemented within the scope of the approach proposed. The analysis of the structures of the obtained forms is carried out as a result their model nature is discovered. Thus, the transition to compositional forms of operations (CFO) made it possible to change the form of representation of computation organization in the processing of two-dimensional data from algorithmic to model one. A general model description of computation organization CFO ( A ijp ( t 11 , t 21 ), CCE i ( j , p )) for various operations of the class in question is given, the distinguishing feature of which is immersion of computations in a parametrized coor-dination-computing environment. The components of the description presented are the algorithms A ijp ( t 11 , t 21 ) obtained as a result of decomposition, immersion into the spatial environment, and time compression of the sequential algorithms A i ( t ), as well as the parameterized coordination-computational environments CCE i ( j , p ). The parameters describing the processing of two-dimensional data x ( t 1 , t 2 ) given on a group H of order N = N 1 × N 2 are presented as follows: t = t 2 + t 1 N 2 , N 1 = h 11 L 1 , N 2 = h 21 L 2 , t 1 = 0, …, N 1 – 1, t 11 = 0, …, h 11 – 1, j 1 = 0, …, L 1 – 1, t 2 = 0, …, N 2 –1, t 21 = 0, …, h 21 – 1, j 2 = 0, …, L 2 – 1. Having moved from the algorithmic form of representing two-dimensional operations to their model form, we not only saw the internal spatial structure of the operations, but also got the opportunity to configure it by parameters h 11 , L 1 and h 21 , L 2 synthesizing various variants of computation organization. Such an opportunity allows one to solve urgent problems aimed at increasing the efficiency of parallel processing, reducing its com-plexity, as well as increasing the flexibility of analysis of the data processed.
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